{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed021cca-4bee-47e1-b896-2ba7d88f2644",
   "metadata": {},
   "source": [
    "# 用ChatGLM2与PaddleOCR构建低配“PP-ChatOCR”\n",
    "\n",
    "PP-ChatOCR是百度飞桨团队推出的一款基于文心大模型的通用图像关键信息抽取工具。它结合了OCR文字识别和大模型技术，可以在多种场景下提取图像中的关键信息，效果相当出色！\n",
    "\n",
    "而本项目的目标则是：使用ChatGLM2-6B模型来进行关键信息抽取处理，实现PP-ChatOCR的功能。\n",
    "\n",
    "跟使用文心大模型进行抽取的PP-ChatOCR相比，本项目的特点是：\n",
    "\n",
    "* 最终效果要比文心大模型差一点\n",
    "* 不需要使用文心大模型API，大模型调用完全自主可控\n",
    "* 代码国产全开源，便于学习和二次开发\n",
    "\n",
    "最终，我们“手撕”一个“低配” 且完全自主可控的“PP-ChatOCR” 。\n",
    "\n",
    "## 缘起 \n",
    "在公开项目里刷到这个项目：[PP-ChatOCR】大模型+小模型，通用关键信息抽取新思路](https://aistudio.baidu.com/aistudio/projectdetail/6488689)，效果相当炸裂。后面在飞桨公众号里也推广了这个项目。\n",
    "\n",
    "但是这个项目需要使用文心大模型，有颇多不便，比如有时候大家并不想自己的信息送到网上，甚至有时候根本就不想联网。作为一个飞桨爱好者，我想到了飞桨PaddleNLP里的开源大模型ChatGLM2-6B，这个模型是当前6B参数量大小模型里效果最好的（萝卜青菜各有所爱，其它模型也很棒），使用ChatGLM2-6B来实现原来文心大模型API部分，应该能实现一个低配版的PP-ChatOCR。\n",
    "\n",
    "说干就干！\n",
    "\n",
    "\n",
    "## 基本技术实现\n",
    "PP-ChatOCR通用文本图像智能分析系统选择了PP-OCRv4和文心大模型串联完成，PP-OCRv4是一个端到端的串联OCR系统，可实现CPU/GPU上的实时预测，在通用场景上达到开源SOTA的效果。\n",
    "\n",
    "参考PP-ChatOCR的实现，我们只要把PP-OCRv4和ChatGLM2-6B串联即可实现低配文本图像智能分析系统。\n",
    "[PP-OCR](https://openi.pcl.ac.cn/PaddlePaddle/PaddleOCR)的文档见：https://openi.pcl.ac.cn/PaddlePaddle/PaddleOCR/src/branch/release/2.6/doc/doc_ch/quickstart.md#user-content-211\n",
    "\n",
    "\n",
    "ChatGLM2-6B的使用可以参考这个项目：[人人都有大模型用！大模型ChatGLM2-6B新手速通！](https://aistudio.baidu.com/aistudio/projectdetail/6519985)\n",
    "\n",
    "然后把两个模型串联起来就行了"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89239bb5-769d-4b72-b10d-dfa5b1a5d67b",
   "metadata": {},
   "source": [
    "# 一、搭建环境\n",
    "## 1、安装PaddleOCR库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "82f18825-1e6c-46bd-bac4-69ac6b2f09e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:45:45.114139Z",
     "iopub.status.busy": "2024-07-08T12:45:45.113435Z",
     "iopub.status.idle": "2024-07-08T12:45:47.958437Z",
     "shell.execute_reply": "2024-07-08T12:45:47.956810Z",
     "shell.execute_reply.started": "2024-07-08T12:45:45.114106Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirror.baidu.com/pypi/simple, https://mirrors.aliyun.com/pypi/simple/\r\n",
      "Requirement already satisfied: paddleocr in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (2.8.0)\r\n",
      "Requirement already satisfied: shapely in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (2.0.4)\r\n",
      "Requirement already satisfied: scikit-image in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (0.24.0)\r\n",
      "Requirement already satisfied: imgaug in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (0.4.0)\r\n",
      "Requirement already satisfied: pyclipper in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (1.3.0.post5)\r\n",
      "Requirement already satisfied: lmdb in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (1.5.1)\r\n",
      "Requirement already satisfied: tqdm in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (4.66.2)\r\n",
      "Requirement already satisfied: numpy<2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (1.26.4)\r\n",
      "Requirement already satisfied: rapidfuzz in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (3.9.4)\r\n",
      "Requirement already satisfied: opencv-python in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (4.9.0.80)\r\n",
      "Requirement already satisfied: opencv-contrib-python in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (4.10.0.84)\r\n",
      "Requirement already satisfied: cython in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (3.0.10)\r\n",
      "Requirement already satisfied: Pillow in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (10.3.0)\r\n",
      "Requirement already satisfied: pyyaml in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (6.0.1)\r\n",
      "Requirement already satisfied: python-docx in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (1.1.2)\r\n",
      "Requirement already satisfied: beautifulsoup4 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (4.12.3)\r\n",
      "Requirement already satisfied: fonttools>=4.24.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (4.51.0)\r\n",
      "Requirement already satisfied: fire>=0.3.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from paddleocr) (0.6.0)\r\n",
      "Requirement already satisfied: six in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from fire>=0.3.0->paddleocr) (1.16.0)\r\n",
      "Requirement already satisfied: termcolor in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from fire>=0.3.0->paddleocr) (2.4.0)\r\n",
      "Requirement already satisfied: soupsieve>1.2 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from beautifulsoup4->paddleocr) (2.5)\r\n",
      "Requirement already satisfied: scipy in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from imgaug->paddleocr) (1.13.0)\r\n",
      "Requirement already satisfied: matplotlib in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from imgaug->paddleocr) (3.8.4)\r\n",
      "Requirement already satisfied: imageio in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from imgaug->paddleocr) (2.34.2)\r\n",
      "Requirement already satisfied: networkx>=2.8 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from scikit-image->paddleocr) (3.3)\r\n",
      "Requirement already satisfied: tifffile>=2022.8.12 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from scikit-image->paddleocr) (2024.7.2)\r\n",
      "Requirement already satisfied: packaging>=21 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from scikit-image->paddleocr) (24.0)\r\n",
      "Requirement already satisfied: lazy-loader>=0.4 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from scikit-image->paddleocr) (0.4)\r\n",
      "Requirement already satisfied: lxml>=3.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from python-docx->paddleocr) (5.2.2)\r\n",
      "Requirement already satisfied: typing-extensions>=4.9.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from python-docx->paddleocr) (4.11.0)\r\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from matplotlib->imgaug->paddleocr) (1.2.1)\r\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from matplotlib->imgaug->paddleocr) (0.12.1)\r\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from matplotlib->imgaug->paddleocr) (1.4.5)\r\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from matplotlib->imgaug->paddleocr) (3.1.2)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from matplotlib->imgaug->paddleocr) (2.9.0.post0)\r\n"
     ]
    }
   ],
   "source": [
    "# 安装PaddleOCR\n",
    "!pip install paddleocr -i https://mirror.baidu.com/pypi/simple"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3675cd71-e4a7-479c-9e35-304a8e6b068e",
   "metadata": {},
   "source": [
    "## 2、下载并解压测试图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a95c503b-4273-4fa4-8d84-d4d5b4f9a757",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:58:05.046032Z",
     "iopub.status.busy": "2024-07-08T12:58:05.045423Z",
     "iopub.status.idle": "2024-07-08T12:58:05.055180Z",
     "shell.execute_reply": "2024-07-08T12:58:05.053975Z",
     "shell.execute_reply.started": "2024-07-08T12:58:05.045996Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ppocr_img.zip OK\r\n",
      "ppocr_img/imgs/a1.jpg OK\r\n"
     ]
    }
   ],
   "source": [
    "# 下载并解压测试图片\n",
    "\n",
    "import os\n",
    "file_path = \"ppocr_img.zip\"\n",
    "if os.path.isfile(file_path):\n",
    "    print(file_path, \"OK\")\n",
    "else:\n",
    "    !wget https://paddleocr.bj.bcebos.com/dygraph_v2.1/ppocr_img.zip\n",
    "\n",
    "# file_path = \"ppocr_img/imgs/00056221.jpg\"\n",
    "# file_path = 'ppocr_img/imgs/00006737.jpg'\n",
    "file_path = 'ppocr_img/imgs/a1.jpg'\n",
    "if os.path.isfile(file_path):\n",
    "    print(file_path, \"OK\")\n",
    "else:\n",
    "    !unzip ppocr_img.zip\n",
    "    print(\"unzip OK!\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e755f1ed-4e8a-458a-969c-a76689c193e7",
   "metadata": {},
   "source": [
    "看一下待处理的图片样子，就是一张很普通的高铁车票照片。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "80030a1a-07e6-4a06-9107-dace1660ff3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:58:06.643054Z",
     "iopub.status.busy": "2024-07-08T12:58:06.642252Z",
     "iopub.status.idle": "2024-07-08T12:58:06.656016Z",
     "shell.execute_reply": "2024-07-08T12:58:06.655201Z",
     "shell.execute_reply.started": "2024-07-08T12:58:06.643014Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "image/jpeg": {
       "width": 640
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython import display\n",
    "display.Image(filename=file_path, width=640)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69744878-60da-482f-a7f8-dbee0d5e933c",
   "metadata": {},
   "source": [
    "## 3、编译安装PaddleNLP最新版\n",
    "当前还没有支持ChatGLM2-6B大模型的PaddleNLP pip安装版本，所以要编译安装。为了加速编译安装，我们采取了先安装paddlenlp==2.6.0rc0版本，再pip删除paddlenlp，然后再编译安装最新版的方法。看着有点绕，相信我，这是当前最快的方法了。\n",
    "\n",
    "等官方新版本的pip安装版本出来，就可以直接使用pip install paddlenlp 这一条命令就行了！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c09ac89b-17c0-4bee-a71d-71049e87e950",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:45:47.986985Z",
     "iopub.status.busy": "2024-07-08T12:45:47.986636Z",
     "iopub.status.idle": "2024-07-08T12:45:51.905724Z",
     "shell.execute_reply": "2024-07-08T12:45:51.904345Z",
     "shell.execute_reply.started": "2024-07-08T12:45:47.986961Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# !git clone https://openi.pcl.ac.cn/PaddlePaddle/PaddleNLP\n",
    "!tar -xzf work/paddlenlp.tar.gz \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1e60e584-b4ae-4b38-ad47-9594f8345f20",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:45:51.907563Z",
     "iopub.status.busy": "2024-07-08T12:45:51.907268Z",
     "iopub.status.idle": "2024-07-08T12:47:24.950534Z",
     "shell.execute_reply": "2024-07-08T12:47:24.949427Z",
     "shell.execute_reply.started": "2024-07-08T12:45:51.907537Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating build/bdist.linux-x86_64/egg/paddlenlp/transformers/dallebart\r\n",
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      "copying build/lib/paddlenlp/transformers/dallebart/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/dallebart\r\n",
      "copying build/lib/paddlenlp/transformers/dallebart/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/dallebart\r\n",
      "copying build/lib/paddlenlp/transformers/dallebart/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/dallebart\r\n",
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      "copying build/lib/paddlenlp/transformers/skep/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/skep\r\n",
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      "copying build/lib/paddlenlp/transformers/megatronbert/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/megatronbert\r\n",
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      "copying build/lib/paddlenlp/transformers/ernie_doc/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_doc\r\n",
      "copying build/lib/paddlenlp/transformers/ernie_doc/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_doc\r\n",
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      "copying build/lib/paddlenlp/transformers/gptj/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/gptj\r\n",
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      "copying build/lib/paddlenlp/transformers/prophetnet/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/prophetnet\r\n",
      "copying build/lib/paddlenlp/transformers/prophetnet/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/prophetnet\r\n",
      "copying build/lib/paddlenlp/transformers/prophetnet/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/prophetnet\r\n",
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      "copying build/lib/paddlenlp/transformers/bloom/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/bloom\r\n",
      "copying build/lib/paddlenlp/transformers/bloom/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/bloom\r\n",
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      "copying build/lib/paddlenlp/transformers/roformerv2/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/roformerv2\r\n",
      "copying build/lib/paddlenlp/transformers/roformerv2/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/roformerv2\r\n",
      "copying build/lib/paddlenlp/transformers/roformerv2/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/roformerv2\r\n",
      "copying build/lib/paddlenlp/transformers/roformerv2/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/roformerv2\r\n",
      "copying build/lib/paddlenlp/transformers/distill_utils.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers\r\n",
      "creating build/bdist.linux-x86_64/egg/paddlenlp/transformers/auto\r\n",
      "copying build/lib/paddlenlp/transformers/auto/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/auto\r\n",
      "copying build/lib/paddlenlp/transformers/auto/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/auto\r\n",
      "copying build/lib/paddlenlp/transformers/auto/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/auto\r\n",
      "copying build/lib/paddlenlp/transformers/auto/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/auto\r\n",
      "copying build/lib/paddlenlp/transformers/auto/processing.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/auto\r\n",
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      "copying build/lib/paddlenlp/transformers/blenderbot/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/blenderbot\r\n",
      "copying build/lib/paddlenlp/transformers/blenderbot/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/blenderbot\r\n",
      "copying build/lib/paddlenlp/transformers/blenderbot/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/blenderbot\r\n",
      "copying build/lib/paddlenlp/transformers/blenderbot/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/blenderbot\r\n",
      "creating build/bdist.linux-x86_64/egg/paddlenlp/transformers/bart\r\n",
      "copying build/lib/paddlenlp/transformers/bart/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/bart\r\n",
      "copying build/lib/paddlenlp/transformers/bart/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/bart\r\n",
      "copying build/lib/paddlenlp/transformers/bart/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/bart\r\n",
      "copying build/lib/paddlenlp/transformers/bart/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/bart\r\n",
      "creating build/bdist.linux-x86_64/egg/paddlenlp/transformers/bit\r\n",
      "copying build/lib/paddlenlp/transformers/bit/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/bit\r\n",
      "copying build/lib/paddlenlp/transformers/bit/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/bit\r\n",
      "copying build/lib/paddlenlp/transformers/bit/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/bit\r\n",
      "copying build/lib/paddlenlp/transformers/bit/image_processing.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/bit\r\n",
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      "copying build/lib/paddlenlp/transformers/transformer/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/transformer\r\n",
      "copying build/lib/paddlenlp/transformers/transformer/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/transformer\r\n",
      "copying build/lib/paddlenlp/transformers/activations.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers\r\n",
      "creating build/bdist.linux-x86_64/egg/paddlenlp/transformers/bert_japanese\r\n",
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      "copying build/lib/paddlenlp/transformers/semantic_search/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/semantic_search\r\n",
      "creating build/bdist.linux-x86_64/egg/paddlenlp/transformers/clap\r\n",
      "copying build/lib/paddlenlp/transformers/clap/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/clap\r\n",
      "copying build/lib/paddlenlp/transformers/clap/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/clap\r\n",
      "copying build/lib/paddlenlp/transformers/clap/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/clap\r\n",
      "copying build/lib/paddlenlp/transformers/clap/processing.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/clap\r\n",
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      "copying build/lib/paddlenlp/transformers/blip_2/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/blip_2\r\n",
      "copying build/lib/paddlenlp/transformers/blip_2/processing.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/blip_2\r\n",
      "copying build/lib/paddlenlp/transformers/blip_2/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/blip_2\r\n",
      "creating build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_vil\r\n",
      "copying build/lib/paddlenlp/transformers/ernie_vil/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_vil\r\n",
      "copying build/lib/paddlenlp/transformers/ernie_vil/processing.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_vil\r\n",
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      "copying build/lib/paddlenlp/transformers/ernie_vil/image_processing.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_vil\r\n",
      "copying build/lib/paddlenlp/transformers/ernie_vil/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_vil\r\n",
      "copying build/lib/paddlenlp/transformers/ernie_vil/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_vil\r\n",
      "copying build/lib/paddlenlp/transformers/ernie_vil/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_vil\r\n",
      "creating build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_layout\r\n",
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      "copying build/lib/paddlenlp/transformers/ernie_layout/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_layout\r\n",
      "copying build/lib/paddlenlp/transformers/ernie_layout/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_layout\r\n",
      "copying build/lib/paddlenlp/transformers/ernie_layout/visual_backbone.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_layout\r\n",
      "copying build/lib/paddlenlp/transformers/ernie_layout/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/ernie_layout\r\n",
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      "copying build/lib/paddlenlp/transformers/visualglm/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/visualglm\r\n",
      "copying build/lib/paddlenlp/transformers/visualglm/processing.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/visualglm\r\n",
      "copying build/lib/paddlenlp/transformers/visualglm/image_processing.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/visualglm\r\n",
      "copying build/lib/paddlenlp/transformers/visualglm/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/visualglm\r\n",
      "copying build/lib/paddlenlp/transformers/visualglm/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/visualglm\r\n",
      "creating build/bdist.linux-x86_64/egg/paddlenlp/transformers/reformer\r\n",
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      "copying build/lib/paddlenlp/transformers/reformer/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/reformer\r\n",
      "copying build/lib/paddlenlp/transformers/reformer/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/reformer\r\n",
      "copying build/lib/paddlenlp/transformers/reformer/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/reformer\r\n",
      "creating build/bdist.linux-x86_64/egg/paddlenlp/transformers/distilbert\r\n",
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      "copying build/lib/paddlenlp/transformers/distilbert/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/distilbert\r\n",
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      "copying build/lib/paddlenlp/transformers/chineseclip/image_processing.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/chineseclip\r\n",
      "copying build/lib/paddlenlp/transformers/chineseclip/modeling.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/chineseclip\r\n",
      "copying build/lib/paddlenlp/transformers/chineseclip/__init__.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/chineseclip\r\n",
      "copying build/lib/paddlenlp/transformers/chineseclip/tokenizer.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/chineseclip\r\n",
      "copying build/lib/paddlenlp/transformers/chineseclip/configuration.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/chineseclip\r\n",
      "copying build/lib/paddlenlp/transformers/chineseclip/processing.py -> build/bdist.linux-x86_64/egg/paddlenlp/transformers/chineseclip\r\n",
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      "creating build/bdist.linux-x86_64/egg/paddlenlp/transformers/albert\r\n",
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      "byte-compiling build/bdist.linux-x86_64/egg/paddlenlp/__init__.py to __init__.cpython-310.pyc\r\n",
      "creating build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying paddlenlp.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying paddlenlp.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying paddlenlp.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying paddlenlp.egg-info/entry_points.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying paddlenlp.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "copying paddlenlp.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO\r\n",
      "zip_safe flag not set; analyzing archive contents...\r\n",
      "paddlenlp.datasets.__pycache__.dataset.cpython-310: module references __path__\r\n",
      "paddlenlp.ops.__pycache__.ext_utils.cpython-310: module references __file__\r\n",
      "paddlenlp.transformers.__pycache__.configuration_utils.cpython-310: module references __file__\r\n",
      "paddlenlp.transformers.__pycache__.generation_utils.cpython-310: module MAY be using inspect.stack\r\n",
      "paddlenlp.transformers.__pycache__.utils.cpython-310: module MAY be using inspect.stack\r\n",
      "paddlenlp.transformers.auto.__pycache__.configuration.cpython-310: module references __file__\r\n",
      "paddlenlp.transformers.layoutxlm.__pycache__.visual_backbone.cpython-310: module references __file__\r\n",
      "creating 'dist/paddlenlp-2.6.0rc0.post0-py3.10.egg' and adding 'build/bdist.linux-x86_64/egg' to it\r\n",
      "removing 'build/bdist.linux-x86_64/egg' (and everything under it)\r\n",
      "Processing paddlenlp-2.6.0rc0.post0-py3.10.egg\r\n",
      "creating /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages/paddlenlp-2.6.0rc0.post0-py3.10.egg\r\n",
      "Extracting paddlenlp-2.6.0rc0.post0-py3.10.egg to /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages\r\n",
      "Adding paddlenlp 2.6.0rc0.post0 to easy-install.pth file\r\n",
      "Installing paddlenlp script to /opt/conda/envs/python35-paddle120-env/bin\r\n",
      "\r\n",
      "Installed /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages/paddlenlp-2.6.0rc0.post0-py3.10.egg\r\n",
      "Processing dependencies for paddlenlp==2.6.0rc0.post0\r\n",
      "Searching for protobuf==3.20.2\r\n",
      "Reading https://pypi.org/simple/protobuf/\r\n",
      "Download error on https://pypi.org/simple/protobuf/: [Errno 99] Cannot assign requested address -- Some packages may not be found!\r\n",
      "Couldn't retrieve index page for 'protobuf'\r\n",
      "Scanning index of all packages (this may take a while)\r\n",
      "Reading https://pypi.org/simple/\r\n",
      "Download error on https://pypi.org/simple/: _ssl.c:980: The handshake operation timed out -- Some packages may not be found!\r\n",
      "No local packages or working download links found for protobuf==3.20.2\r\n",
      "error: Could not find suitable distribution for Requirement.parse('protobuf==3.20.2')\r\n"
     ]
    }
   ],
   "source": [
    "!pip install paddlenlp==2.6.0rc0 -i https://mirror.baidu.com/pypi/simple\n",
    "!pip uninstall paddlenlp -y\n",
    "!pip install \"onnx>=1.10.0\" -i https://mirror.baidu.com/pypi/simple\n",
    "!cd PaddleNLP && python setup.py install "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ec926f7-59c8-4b51-86af-14726b6ba351",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:47:24.952604Z",
     "iopub.status.busy": "2024-07-08T12:47:24.952019Z",
     "iopub.status.idle": "2024-07-08T12:47:26.120629Z",
     "shell.execute_reply": "2024-07-08T12:47:26.119553Z",
     "shell.execute_reply.started": "2024-07-08T12:47:24.952570Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "paddle2onnx                1.2.1\r\n",
      "paddlefsl                  1.1.0\r\n",
      "paddlehub                  2.4.0\r\n",
      "paddlenlp                  2.6.0rc0.post0\r\n",
      "paddleocr                  2.8.0\r\n",
      "paddlepaddle-gpu           2.5.2\r\n"
     ]
    }
   ],
   "source": [
    "!pip list |grep paddle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f2e261-ed08-46a0-af41-9aa047fed0a5",
   "metadata": {},
   "source": [
    "## 4、将ChatGLM2-6B模型文件放入指定位置\n",
    "ChatGLM2-6B的模型文件已经通过数据集挂载，使用tar命令解到指定位置即可，因为模型文件非常大，约12G大小，这样速度最快。这里模型放置在.paddlenlp/models/THUDM/chatglm2-6b目录，这个目录是Taskflow等飞桨套件放置ChatGLM2-6B的模型文件的默认目录。\n",
    "\n",
    "大约耗时3-4分钟。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "561f74d3-477e-40b1-8ca7-a25f7ecd6ff4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:47:26.122461Z",
     "iopub.status.busy": "2024-07-08T12:47:26.122024Z",
     "iopub.status.idle": "2024-07-08T12:47:26.507583Z",
     "shell.execute_reply": "2024-07-08T12:47:26.506128Z",
     "shell.execute_reply.started": "2024-07-08T12:47:26.122430Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".paddlenlp/models/THUDM/chatglm2-6b/model_state.pdparams\r\n",
      "already OK!\r\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "file_path = \".paddlenlp/models/THUDM/chatglm2-6b/model_state.pdparams\"\n",
    "if os.path.isfile(file_path):\n",
    "    !ls $file_path\n",
    "    print(\"already OK!\")\n",
    "else:\n",
    "    print(\"File not found. Starting tar...\")\n",
    "    !tar -xzvf /home/aistudio/data/data233533/chatglm2-6b-8.1.tar.gz\n",
    "    print(\"OK!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d42b99c5-6e46-4bf1-abc7-34cf32f2095e",
   "metadata": {},
   "source": [
    "# 二、低配版PP-ChatOCR实践\n",
    "\n",
    "## 1、导入PaddleNLP（ChatGLM2）相关库，定义推理函数\n",
    "第一次运行的时候，会报错：No module named 'paddlenlp' ，这时候重启内核，再次“运行全部Cell”即可。\n",
    "\n",
    "定义好predictor推理函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c5d0d691-ef82-4836-98dc-a82b12651f22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:47:26.509856Z",
     "iopub.status.busy": "2024-07-08T12:47:26.509540Z",
     "iopub.status.idle": "2024-07-08T12:49:23.542725Z",
     "shell.execute_reply": "2024-07-08T12:49:23.541603Z",
     "shell.execute_reply.started": "2024-07-08T12:47:26.509828Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\r\n",
      "  from .autonotebook import tqdm as notebook_tqdm\r\n",
      "/opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages/_distutils_hack/__init__.py:33: UserWarning: Setuptools is replacing distutils.\r\n",
      "  warnings.warn(\"Setuptools is replacing distutils.\")\r\n",
      "\u001b[32m[2024-07-08 20:47:31,375] [    INFO]\u001b[0m - Found /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/tokenizer_config.json\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,377] [    INFO]\u001b[0m - We are using <class 'paddlenlp.transformers.chatglm_v2.tokenizer.ChatGLMv2Tokenizer'> to load 'THUDM/chatglm2-6b'.\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,378] [    INFO]\u001b[0m - Already cached /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/tokenizer.model\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,379] [    INFO]\u001b[0m - Already cached /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/added_tokens.json\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,380] [    INFO]\u001b[0m - Already cached /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/special_tokens_map.json\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,380] [    INFO]\u001b[0m - Already cached /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/tokenizer_config.json\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,477] [    INFO]\u001b[0m - Found /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/config.json\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,480] [    INFO]\u001b[0m - We are using <class 'paddlenlp.transformers.chatglm_v2.configuration.ChatGLMv2Config'> to load 'THUDM/chatglm2-6b'.\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,536] [    INFO]\u001b[0m - Found /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/config.json\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,538] [    INFO]\u001b[0m - Loading configuration file /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/config.json\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,593] [    INFO]\u001b[0m - Found /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/config.json\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,597] [    INFO]\u001b[0m - We are using <class 'paddlenlp.transformers.chatglm_v2.modeling.ChatGLMv2ForCausalLM'> to load 'THUDM/chatglm2-6b'.\u001b[0m\r\n",
      "\u001b[33m[2024-07-08 20:47:31,599] [ WARNING]\u001b[0m - `load_state_as_np` is deprecated,  please delete it!\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,650] [    INFO]\u001b[0m - Found /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/config.json\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,652] [    INFO]\u001b[0m - Loading configuration file /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/config.json\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,751] [    INFO]\u001b[0m - Already cached /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/model_state.pdparams\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:47:31,753] [    INFO]\u001b[0m - Loading weights file model_state.pdparams from cache at /home/aistudio/.paddlenlp/models/THUDM/chatglm2-6b/model_state.pdparams\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:49:00,994] [    INFO]\u001b[0m - Loaded weights file from disk, setting weights to model.\u001b[0m\r\n",
      "W0708 20:49:00.999967 47136 gpu_resources.cc:119] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 12.0, Runtime API Version: 11.8\r\n",
      "W0708 20:49:01.001530 47136 gpu_resources.cc:149] device: 0, cuDNN Version: 8.9.\r\n",
      "\u001b[32m[2024-07-08 20:49:22,541] [    INFO]\u001b[0m - All model checkpoint weights were used when initializing ChatGLMv2ForCausalLM.\r\n",
      "\u001b[0m\r\n",
      "\u001b[32m[2024-07-08 20:49:22,542] [    INFO]\u001b[0m - All the weights of ChatGLMv2ForCausalLM were initialized from the model checkpoint at THUDM/chatglm2-6b.\r\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use ChatGLMv2ForCausalLM for predictions without further training.\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "from PaddleNLP.llm.chatglm.predict_generation import Predictor\n",
    "\n",
    "import argparse\n",
    "import sys\n",
    "def parse_arguments():\n",
    "    import argparse\n",
    "    sys.argv = ['program_name', '--seed', '42']\n",
    "    \n",
    "    parser = argparse.ArgumentParser()\n",
    "    parser.add_argument(\"--model_name_or_path\", default=\"THUDM/chatglm2-6b\", help=\"The directory of model.\")\n",
    "    parser.add_argument(\n",
    "        \"--merge_tensor_parallel_path\", default=None, help=\"The directory of model to merge tensor parallel parts.\"\n",
    "    )\n",
    "    parser.add_argument(\"--batch_size\", type=int, default=1, help=\"The batch size of data.\")\n",
    "    parser.add_argument(\"--src_length\", type=int, default=1024, help=\"The batch size of data.\")\n",
    "    parser.add_argument(\"--tgt_length\", type=int, default=1024, help=\"The batch size of data.\")\n",
    "    parser.add_argument(\"--lora_path\", default=None, help=\"The directory of LoRA parameters. Default to None\")\n",
    "    parser.add_argument(\n",
    "        \"--prefix_path\", default=None, help=\"The directory of Prefix Tuning parameters. Default to None\"\n",
    "    )\n",
    "    parser.add_argument(\"--data_file\", default=None, help=\"data file directory\")\n",
    "    parser.add_argument(\"--predict_file\", default=\"prediction.json\", help=\"predict result file directory\")\n",
    "    parser.add_argument(\"--device\", type=str, default=\"gpu\", help=\"Device\")\n",
    "    parser.add_argument(\"--seed\", type=int, default=42, help=\"The seed of data.\")\n",
    "    return parser.parse_args()\n",
    "args = parse_arguments()\n",
    "predictor = Predictor(args)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20468afa-59ba-4d99-bdb1-3816d33cff73",
   "metadata": {},
   "source": [
    "## 2、图片OCR识别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "224ee90b-8dfc-4947-8b5b-522b7b225c06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:49:23.544814Z",
     "iopub.status.busy": "2024-07-08T12:49:23.544112Z",
     "iopub.status.idle": "2024-07-08T12:49:27.837863Z",
     "shell.execute_reply": "2024-07-08T12:49:27.836722Z",
     "shell.execute_reply.started": "2024-07-08T12:49:23.544783Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_det_infer.tar to /home/aistudio/.paddleocr/whl/det/ch/ch_PP-OCRv4_det_infer/ch_PP-OCRv4_det_infer.tar\r\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4.89M/4.89M [00:00<00:00, 17.0MiB/s]\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download https://paddleocr.bj.bcebos.com/PP-OCRv4/chinese/ch_PP-OCRv4_rec_infer.tar to /home/aistudio/.paddleocr/whl/rec/ch/ch_PP-OCRv4_rec_infer/ch_PP-OCRv4_rec_infer.tar\r\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 11.0M/11.0M [00:00<00:00, 33.1MiB/s]\r\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar to /home/aistudio/.paddleocr/whl/cls/ch_ppocr_mobile_v2.0_cls_infer/ch_ppocr_mobile_v2.0_cls_infer.tar\r\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2.19M/2.19M [00:00<00:00, 45.6MiB/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024/07/08 20:49:24] ppocr DEBUG: Namespace(help='==SUPPRESS==', use_gpu=True, use_xpu=False, use_npu=False, use_mlu=False, ir_optim=True, use_tensorrt=False, min_subgraph_size=15, precision='fp32', gpu_mem=500, gpu_id=0, image_dir=None, page_num=0, det_algorithm='DB', det_model_dir='/home/aistudio/.paddleocr/whl/det/ch/ch_PP-OCRv4_det_infer', det_limit_side_len=960, det_limit_type='max', det_box_type='quad', det_db_thresh=0.3, det_db_box_thresh=0.6, det_db_unclip_ratio=1.5, max_batch_size=10, use_dilation=False, det_db_score_mode='fast', det_east_score_thresh=0.8, det_east_cover_thresh=0.1, det_east_nms_thresh=0.2, det_sast_score_thresh=0.5, det_sast_nms_thresh=0.2, det_pse_thresh=0, det_pse_box_thresh=0.85, det_pse_min_area=16, det_pse_scale=1, scales=[8, 16, 32], alpha=1.0, beta=1.0, fourier_degree=5, rec_algorithm='SVTR_LCNet', rec_model_dir='/home/aistudio/.paddleocr/whl/rec/ch/ch_PP-OCRv4_rec_infer', rec_image_inverse=True, rec_image_shape='3, 48, 320', rec_batch_num=6, max_text\r\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r\n"
     ]
    }
   ],
   "source": [
    "from paddleocr import PaddleOCR, draw_ocr\n",
    "\n",
    "# Paddleocr目前支持的多语言语种可以通过修改lang参数进行切换\n",
    "# 例如`ch`, `en`, `fr`, `german`, `korean`, `japan`\n",
    "ocr = PaddleOCR(use_angle_cls=True, lang=\"ch\")  # need to run only once to download and load model into memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "be9947e6-08c6-4c60-b01b-6d78fb0d806e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:49:27.841803Z",
     "iopub.status.busy": "2024-07-08T12:49:27.840943Z",
     "iopub.status.idle": "2024-07-08T12:49:32.811228Z",
     "shell.execute_reply": "2024-07-08T12:49:32.809836Z",
     "shell.execute_reply.started": "2024-07-08T12:49:27.841769Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2024/07/08 20:49:31] ppocr DEBUG: dt_boxes num : 83, elapsed : 3.8766870498657227\r\n",
      "[2024/07/08 20:49:32] ppocr DEBUG: cls num  : 83, elapsed : 0.2758936882019043\r\n",
      "[2024/07/08 20:49:32] ppocr DEBUG: rec_res num  : 83, elapsed : 0.7140562534332275\r\n",
      "[[[232.0, 210.0], [306.0, 210.0], [306.0, 247.0], [232.0, 247.0]], ('附件：', 0.9837391972541809)]\r\n",
      "[[[288.0, 271.0], [1046.0, 265.0], [1046.0, 310.0], [288.0, 316.0]], ('华南理工大学家庭经济困难本科生认定申请表', 0.9877155423164368)]\r\n",
      "[[[228.0, 316.0], [475.0, 310.0], [476.0, 359.0], [229.0, 365.0]], ('院系：物理专业：', 0.9376285076141357)]\r\n",
      "[[[479.0, 308.0], [838.0, 318.0], [837.0, 365.0], [478.0, 355.0]], ('物2里年级：2022班级：', 0.8677949905395508)]\r\n",
      "[[[932.0, 316.0], [1245.0, 306.0], [1246.0, 347.0], [933.0, 357.0]], ('学号：202204347298', 0.9234928488731384)]\r\n",
      "[[[258.0, 363.0], [440.0, 363.0], [440.0, 404.0], [258.0, 404.0]], ('姓名霍斤', 0.8333567380905151)]\r\n",
      "[[[516.0, 355.0], [671.0, 351.0], [672.0, 398.0], [517.0, 403.0]], ('性别勇', 0.7795005440711975)]\r\n",
      "[[[734.0, 363.0], [826.0, 363.0], [826.0, 392.0], [734.0, 392.0]], ('出生年月', 0.9999158382415771)]\r\n",
      "[[[876.0, 359.0], [988.0, 359.0], [988.0, 394.0], [876.0, 394.0]], ('2004108', 0.9573414921760559)]\r\n",
      "[[[1024.0, 361.0], [1078.0, 361.0], [1078.0, 392.0], [1024.0, 392.0]], ('民族', 0.9999091625213623)]\r\n",
      "[[[1096.0, 357.0], [1168.0, 357.0], [1168.0, 396.0], [1096.0, 396.0]], ('汉', 0.6902267932891846)]\r\n",
      "[[[164.0, 382.0], [236.0, 382.0], [236.0, 441.0], [164.0, 441.0]], ('茶', 0.8382037878036499)]\r\n",
      "[[[855.0, 404.0], [1030.0, 408.0], [1029.0, 450.0], [854.0, 445.0]], ('城镇口农村', 0.9539766311645508)]\r\n",
      "[[[618.0, 435.0], [670.0, 435.0], [670.0, 459.0], [618.0, 459.0]], ('性质', 0.9979720115661621)]\r\n",
      "[[[289.0, 469.0], [389.0, 464.0], [391.0, 500.0], [291.0, 505.0]], ('户籍地址', 0.9997283220291138)]\r\n",
      "[[[430.0, 461.0], [870.0, 461.0], [870.0, 508.0], [430.0, 508.0]], ('东省(自治区)小市海(市、区)', 0.9027208089828491)]\r\n",
      "[[[907.0, 463.0], [1017.0, 458.0], [1019.0, 494.0], [909.0, 499.0]], ('镇（街道）', 0.9670912623405457)]\r\n",
      "[[[1062.0, 465.0], [1178.0, 465.0], [1178.0, 498.0], [1062.0, 498.0]], ('村（居委）', 0.9969860911369324)]\r\n",
      "[[[270.0, 508.0], [408.0, 508.0], [408.0, 541.0], [270.0, 541.0]], ('家长手机号码', 0.998701274394989)]\r\n",
      "[[[436.0, 510.0], [610.0, 510.0], [610.0, 545.0], [436.0, 545.0]], ('14290482098', 0.9423568248748779)]\r\n",
      "[[[646.0, 508.0], [804.0, 508.0], [804.0, 541.0], [646.0, 541.0]], ('家庭人均年收入', 0.9997301697731018)]\r\n",
      "[[[1040.0, 508.0], [1092.0, 508.0], [1092.0, 539.0], [1040.0, 539.0]], ('（元）', 0.9905840754508972)]\r\n",
      "[[[282.0, 555.0], [400.0, 555.0], [400.0, 588.0], [282.0, 588.0]], ('家庭人口数', 0.9986589550971985)]\r\n",
      "[[[518.0, 553.0], [554.0, 553.0], [554.0, 586.0], [518.0, 586.0]], ('Z', 0.7947239279747009)]\r\n",
      "[[[636.0, 553.0], [816.0, 553.0], [816.0, 586.0], [636.0, 586.0]], ('家庭成员在学人数', 0.9990414381027222)]\r\n",
      "[[[184.0, 573.0], [218.0, 573.0], [218.0, 608.0], [184.0, 608.0]], ('家', 0.9999444484710693)]\r\n",
      "[[[182.0, 604.0], [218.0, 604.0], [218.0, 641.0], [182.0, 641.0]], ('庭', 0.9970333576202393)]\r\n",
      "[[[294.0, 600.0], [388.0, 600.0], [388.0, 630.0], [294.0, 630.0]], ('赠养人数', 0.9854063987731934)]\r\n",
      "[[[638.0, 596.0], [818.0, 596.0], [818.0, 630.0], [638.0, 630.0]], ('家庭成员失业人数', 0.9986854195594788)]\r\n",
      "[[[182.0, 637.0], [216.0, 637.0], [216.0, 673.0], [182.0, 673.0]], ('情', 0.9994966983795166)]\r\n",
      "[[[252.0, 641.0], [1178.0, 637.0], [1178.0, 671.0], [252.0, 675.0]], ('1.脱贫家庭学生2.脱贫不稳定家庭学生3.边缘易致贫家庭学生4.最低生活保障家庭学生5.最低', 0.99767005443573)]\r\n",
      "[[[184.0, 675.0], [212.0, 675.0], [212.0, 704.0], [184.0, 704.0]], ('况', 0.9998149275779724)]\r\n",
      "[[[252.0, 675.0], [1180.0, 671.0], [1180.0, 704.0], [252.0, 708.0]], ('生活保障边缘家庭学生6.支出型困难家庭学生7.特困供养学生8.孤儿（含事实无人抚养）9.享', 0.9966588616371155)]\r\n",
      "[[[252.0, 708.0], [1180.0, 704.0], [1180.0, 737.0], [252.0, 741.0]], ('受国家定期抚恤补贴的优抚对象（含烈士子女、牺牲军人子女）10.因公牺牲警察子女11.特困职', 0.9708215594291687)]\r\n",
      "[[[256.0, 743.0], [728.0, 743.0], [728.0, 771.0], [256.0, 771.0]], ('工子女12.家庭经济困难残疾学生13.残疾人子女', 0.9977852702140808)]\r\n",
      "[[[252.0, 779.0], [598.0, 779.0], [598.0, 806.0], [252.0, 806.0]], ('如符合上述类型，请填写相应数字：', 0.9983052015304565)]\r\n",
      "[[[442.0, 818.0], [522.0, 818.0], [522.0, 853.0], [442.0, 853.0]], ('与学生', 0.9999022483825684)]\r\n",
      "[[[550.0, 820.0], [684.0, 820.0], [684.0, 853.0], [550.0, 853.0]], ('工作（学习）', 0.9953830242156982)]\r\n",
      "[[[982.0, 820.0], [1054.0, 820.0], [1054.0, 849.0], [982.0, 849.0]], ('年收入', 0.9999280571937561)]\r\n",
      "[[[266.0, 838.0], [318.0, 838.0], [318.0, 869.0], [266.0, 869.0]], ('姓名', 0.9994133710861206)]\r\n",
      "[[[362.0, 838.0], [414.0, 838.0], [414.0, 869.0], [362.0, 869.0]], ('年龄', 0.9999909400939941)]\r\n",
      "[[[732.0, 834.0], [830.0, 834.0], [830.0, 869.0], [732.0, 869.0]], ('从业情况', 0.9994351863861084)]\r\n",
      "[[[860.0, 834.0], [956.0, 834.0], [956.0, 869.0], [860.0, 869.0]], ('文化程度', 0.9998675584793091)]\r\n",
      "[[[1082.0, 836.0], [1174.0, 836.0], [1174.0, 865.0], [1082.0, 865.0]], ('健康状况', 0.9992387890815735)]\r\n",
      "[[[180.0, 859.0], [216.0, 860.0], [212.0, 1063.0], [176.0, 1063.0]], ('主要家庭成员情况', 0.99927818775177)]\r\n",
      "[[[452.0, 853.0], [508.0, 853.0], [508.0, 885.0], [452.0, 885.0]], ('关系', 0.999744713306427)]\r\n",
      "[[[596.0, 853.0], [648.0, 853.0], [648.0, 885.0], [596.0, 885.0]], ('单位', 0.9999707937240601)]\r\n",
      "[[[990.0, 851.0], [1048.0, 851.0], [1048.0, 883.0], [990.0, 883.0]], ('（元）', 0.9912248253822327)]\r\n",
      "[[[246.0, 892.0], [326.0, 892.0], [326.0, 936.0], [246.0, 936.0]], ('霍久', 0.6002540588378906)]\r\n",
      "[[[451.0, 890.0], [513.0, 895.0], [509.0, 936.0], [447.0, 931.0]], ('仅子', 0.8115161657333374)]\r\n",
      "[[[540.0, 887.0], [664.0, 887.0], [664.0, 936.0], [540.0, 936.0]], ('服务员', 0.9585144519805908)]\r\n",
      "[[[856.0, 885.0], [930.0, 885.0], [930.0, 932.0], [856.0, 932.0]], ('中学', 0.9954414963722229)]\r\n",
      "[[[358.0, 898.0], [410.0, 898.0], [410.0, 930.0], [358.0, 930.0]], ('57', 0.9341959953308105)]\r\n",
      "[[[980.0, 891.0], [1062.0, 891.0], [1062.0, 928.0], [980.0, 928.0]], ('5000', 0.9808327555656433)]\r\n",
      "[[[588.0, 1116.0], [638.0, 1116.0], [638.0, 1157.0], [588.0, 1157.0]], ('无', 0.9541980624198914)]\r\n",
      "[[[1070.0, 1114.0], [1130.0, 1114.0], [1130.0, 1153.0], [1070.0, 1153.0]], ('无', 0.9443116188049316)]\r\n",
      "[[[250.0, 1130.0], [484.0, 1130.0], [484.0, 1163.0], [250.0, 1163.0]], ('家庭遭受自然灾害情况：', 0.9876508712768555)]\r\n",
      "[[[700.0, 1130.0], [938.0, 1130.0], [938.0, 1157.0], [700.0, 1157.0]], ('家庭遭受突发意外事件：', 0.9863848090171814)]\r\n",
      "[[[168.0, 1151.0], [222.0, 1151.0], [222.0, 1187.0], [168.0, 1187.0]], ('影响', 0.9998019933700562)]\r\n",
      "[[[686.0, 1154.0], [1071.0, 1149.0], [1072.0, 1216.0], [687.0, 1220.0]], ('霍斤得了期怕金标症', 0.7820677161216736)]\r\n",
      "[[[168.0, 1185.0], [222.0, 1185.0], [222.0, 1220.0], [168.0, 1220.0]], ('家庭', 0.9991488456726074)]\r\n",
      "[[[244.0, 1181.0], [668.0, 1177.0], [668.0, 1210.0], [244.0, 1214.0]], ('家庭成员因残疾、年迈而劳动能力弱情况：', 0.9984267354011536)]\r\n",
      "[[[168.0, 1218.0], [218.0, 1218.0], [218.0, 1251.0], [168.0, 1251.0]], ('经济', 0.9996168613433838)]\r\n",
      "[[[536.0, 1216.0], [588.0, 1216.0], [588.0, 1259.0], [536.0, 1259.0]], ('元', 0.6220378875732422)]\r\n",
      "[[[999.0, 1217.0], [1120.0, 1209.0], [1123.0, 1253.0], [1002.0, 1260.0]], ('5万元', 0.9797518849372864)]\r\n",
      "[[[246.0, 1230.0], [438.0, 1230.0], [438.0, 1263.0], [246.0, 1263.0]], ('家庭成员失业情况：', 0.9975566864013672)]\r\n",
      "[[[682.0, 1230.0], [841.0, 1226.0], [842.0, 1261.0], [683.0, 1265.0]], ('，家庭欠债情况：', 0.9691676497459412)]\r\n",
      "[[[170.0, 1251.0], [218.0, 1251.0], [218.0, 1285.0], [170.0, 1285.0]], ('状况', 0.9994111657142639)]\r\n",
      "[[[548.0, 1267.0], [610.0, 1267.0], [610.0, 1316.0], [548.0, 1316.0]], ('无', 0.8866299390792847)]\r\n",
      "[[[170.0, 1283.0], [220.0, 1283.0], [220.0, 1316.0], [170.0, 1316.0]], ('有关', 0.9995583891868591)]\r\n",
      "[[[248.0, 1283.0], [352.0, 1283.0], [352.0, 1318.0], [248.0, 1318.0]], ('其他情况：', 0.9998441934585571)]\r\n",
      "[[[170.0, 1314.0], [218.0, 1314.0], [218.0, 1348.0], [170.0, 1348.0]], ('信息', 0.9989935159683228)]\r\n",
      "[[[248.0, 1391.0], [344.0, 1391.0], [344.0, 1420.0], [248.0, 1420.0]], ('承诺内容：', 0.9945757985115051)]\r\n",
      "[[[256.0, 1463.0], [446.0, 1463.0], [446.0, 1520.0], [256.0, 1520.0]], ('求所信属实', 0.6988173723220825)]\r\n",
      "[[[180.0, 1476.0], [216.0, 1475.0], [220.0, 1582.0], [184.0, 1583.0]], ('个人承诺', 0.9988717436790466)]\r\n",
      "[[[728.0, 1489.0], [930.0, 1489.0], [930.0, 1522.0], [728.0, 1522.0]], ('街道办或村委会联系', 0.9989612102508545)]\r\n",
      "[[[956.0, 1505.0], [1177.0, 1500.0], [1178.0, 1541.0], [957.0, 1546.0]], ('17320982077', 0.9641156792640686)]\r\n",
      "[[[760.0, 1522.0], [890.0, 1522.0], [890.0, 1557.0], [760.0, 1557.0]], ('电话（必填）', 0.9726426601409912)]\r\n",
      "[[[250.0, 1591.0], [474.0, 1591.0], [474.0, 1640.0], [250.0, 1640.0]], ('学生本人签名：霍斤', 0.9578980207443237)]\r\n",
      "[[[496.0, 1587.0], [706.0, 1587.0], [706.0, 1628.0], [496.0, 1628.0]], ('2024年04月21日', 0.9289912581443787)]\r\n"
     ]
    }
   ],
   "source": [
    "# img_path = 'ppocr_img/imgs/00056221.jpg'\n",
    "# img_path = 'ppocr_img/imgs/00006737.jpg'\n",
    "img_path = 'ppocr_img/imgs/a1.jpg'\n",
    "result = ocr.ocr(img_path, cls=True)\n",
    "for idx in range(len(result)):\n",
    "    res = result[idx]\n",
    "    for line in res:\n",
    "        print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0240b6f5-2803-468b-bd79-2037c38af1bf",
   "metadata": {},
   "source": [
    "直接将ocr输出结果进行文本分析和抽取，效果不太好，而且无关内容太多，影响大模型的处理速度，于是我们将识别出的内容整理出来，以便后续使用。\n",
    "\n",
    "可以看到输出的ocr_result变量就是ocr识别的所有文字。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d8403279-54d2-404e-a9d7-ca22d31c187d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:49:32.813110Z",
     "iopub.status.busy": "2024-07-08T12:49:32.812710Z",
     "iopub.status.idle": "2024-07-08T12:49:32.819300Z",
     "shell.execute_reply": "2024-07-08T12:49:32.818193Z",
     "shell.execute_reply.started": "2024-07-08T12:49:32.813079Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 附件： 华南理工大学家庭经济困难本科生认定申请表 院系：物理专业： 物2里年级：2022班级： 学号：202204347298 姓名霍斤 性别勇 出生年月 2004108 民族 汉 茶 城镇口农村 性质 户籍地址 东省(自治区)小市海(市、区) 镇（街道） 村（居委） 家长手机号码 14290482098 家庭人均年收入 （元） 家庭人口数 Z 家庭成员在学人数 家 庭 赠养人数 家庭成员失业人数 情 1.脱贫家庭学生2.脱贫不稳定家庭学生3.边缘易致贫家庭学生4.最低生活保障家庭学生5.最低 况 生活保障边缘家庭学生6.支出型困难家庭学生7.特困供养学生8.孤儿（含事实无人抚养）9.享 受国家定期抚恤补贴的优抚对象（含烈士子女、牺牲军人子女）10.因公牺牲警察子女11.特困职 工子女12.家庭经济困难残疾学生13.残疾人子女 如符合上述类型，请填写相应数字： 与学生 工作（学习） 年收入 姓名 年龄 从业情况 文化程度 健康状况 主要家庭成员情况 关系 单位 （元） 霍久 仅子 服务员 中学 57 5000 无 无 家庭遭受自然灾害情况： 家庭遭受突发意外事件： 影响 霍斤得了期怕金标症 家庭 家庭成员因残疾、年迈而劳动能力弱情况： 经济 元 5万元 家庭成员失业情况： ，家庭欠债情况： 状况 无 有关 其他情况： 信息 承诺内容： 求所信属实 个人承诺 街道办或村委会联系 17320982077 电话（必填） 学生本人签名：霍斤 2024年04月21日\r\n"
     ]
    }
   ],
   "source": [
    "ocr_result = \"\"\n",
    "for idx in range(len(result)):\n",
    "    res = result[idx]\n",
    "    for line in res:\n",
    "#         print(line[1][0])\n",
    "        ocr_result = ocr_result + \" \" + str(line[1][0])\n",
    "#         break\n",
    "print(ocr_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca95fd51-3efb-454f-b925-37b42ab8edf5",
   "metadata": {},
   "source": [
    "## 3、写提示词\n",
    "我们将ocr识别的字符通过ocr_result变量传入提示词prompt变量中，需要检索的信息通过key变量定义，并传入提示词prompt变量中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "590d6d97-a23a-46cd-82f0-db71598365fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:49:32.820972Z",
     "iopub.status.busy": "2024-07-08T12:49:32.820596Z",
     "iopub.status.idle": "2024-07-08T12:49:32.827014Z",
     "shell.execute_reply": "2024-07-08T12:49:32.826046Z",
     "shell.execute_reply.started": "2024-07-08T12:49:32.820946Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你现在的任务是从OCR文字识别的结果中提取我指定的关键信息。OCR的文字识别结果使用```符号包围，包含所识别出来的文字，\r\n",
      "                顺序在原始图片中从左至右、从上至下。我指定的关键信息使用[]符号包围。请注意OCR的文字识别结果可能存在长句子换行被切断、不合理的分词、\r\n",
      "                对应错位等问题，你需要结合上下文语义进行综合判断，以抽取准确的关键信息。输出为json格式。\r\n",
      "                下面正式开始：\r\n",
      "                OCR文字：``` 附件： 华南理工大学家庭经济困难本科生认定申请表 院系：物理专业： 物2里年级：2022班级： 学号：202204347298 姓名霍斤 性别勇 出生年月 2004108 民族 汉 茶 城镇口农村 性质 户籍地址 东省(自治区)小市海(市、区) 镇（街道） 村（居委） 家长手机号码 14290482098 家庭人均年收入 （元） 家庭人口数 Z 家庭成员在学人数 家 庭 赠养人数 家庭成员失业人数 情 1.脱贫家庭学生2.脱贫不稳定家庭学生3.边缘易致贫家庭学生4.最低生活保障家庭学生5.最低 况 生活保障边缘家庭学生6.支出型困难家庭学生7.特困供养学生8.孤儿（含事实无人抚养）9.享 受国家定期抚恤补贴的优抚对象（含烈士子女、牺牲军人子女）10.因公牺牲警察子女11.特困职 工子女12.家庭经济困难残疾学生13.残疾人子女 如符合上述类型，请填写相应数字： 与学生 工作（学习） 年收入 姓名 年龄 从业情况 文化程度 健康状况 主要家庭成员情况 关系 单位 （元） 霍久 仅子 服务员 中学 57 5000 无 无 家庭遭受自然灾害情况： 家庭遭受突发意外事件： 影响 霍斤得了期怕金标症 家庭 家庭成员因残疾、年迈而劳动能力弱情况： 经济 元 5万元 家庭成员失业情况： ，家庭欠债情况： 状况 无 有关 其他情况： 信息 承诺内容： 求所信属实 个人承诺 街道办或村委会联系 17320982077 电话（必填） 学生本人签名：霍斤 2024年04月21日```\r\n",
      "                要抽取的关键信息：[姓名 时间]。\r\n"
     ]
    }
   ],
   "source": [
    "# key = \"目的地 日期 始发地 登机口\"\n",
    "\n",
    "key = \"姓名 时间\"\n",
    "prompt = f\"\"\"你现在的任务是从OCR文字识别的结果中提取我指定的关键信息。OCR的文字识别结果使用```符号包围，包含所识别出来的文字，\n",
    "                顺序在原始图片中从左至右、从上至下。我指定的关键信息使用[]符号包围。请注意OCR的文字识别结果可能存在长句子换行被切断、不合理的分词、\n",
    "                对应错位等问题，你需要结合上下文语义进行综合判断，以抽取准确的关键信息。输出为json格式。\n",
    "                下面正式开始：\n",
    "                OCR文字：```{ocr_result}```\n",
    "                要抽取的关键信息：[{key}]。\"\"\"\n",
    "print(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a3181862-88e1-4a45-a0a5-307722807b4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:55:50.156484Z",
     "iopub.status.busy": "2024-07-08T12:55:50.155675Z",
     "iopub.status.idle": "2024-07-08T12:55:50.163324Z",
     "shell.execute_reply": "2024-07-08T12:55:50.162092Z",
     "shell.execute_reply.started": "2024-07-08T12:55:50.156416Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "prompt = f\"\"\"\n",
    "你现在的任务是从OCR文字识别的结果中提取我指定的关键信息。OCR的文字识别结果使用```符号包围，包含所识别出来的文字，\n",
    "顺序在原始图片中从左至右、从上至下。我指定的关键信息使用[]符号包围。请注意OCR的文字识别结果可能存在长句子换行被切断、不合理的分词、\n",
    "对应错位等问题，你需要结合上下文语义进行综合判断，以抽取准确的关键信息。在返回结果时使用json格式，包含一个key-value对，key值为我指定的关键信息，value值为所抽取的结果。\n",
    "如果认为OCR识别结果中没有关键信息key，则将value赋值为“未找到相关信息”。 请只输出json格式的结果，不要包含其它多余文字！\n",
    "下面正式开始：\n",
    "OCR文字：```{ocr_result}```\n",
    "要抽取的关键信息：[院系, 专业, 年级, 班级, 学号, 姓名, 性别, 出生年月, 民族, 主要家庭成员情况, 家庭遭受自然灾害情况, 家庭遭受突发意外事件, 家庭成员因残疾年迈而劳动能力弱情况, 家庭欠债情况, 承诺内容, 街道办或村委会联系, 学生本人签名, 日期]。\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2380f0b1-ed69-4619-ab8d-b0fb17bb4c8b",
   "metadata": {},
   "source": [
    "再写一个抽取4个关键字：姓名 时间 乘车的车次 到站 的提示词"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8210ee8a-cccc-4771-9907-f27982a74dfa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:49:32.835618Z",
     "iopub.status.busy": "2024-07-08T12:49:32.835051Z",
     "iopub.status.idle": "2024-07-08T12:49:32.840898Z",
     "shell.execute_reply": "2024-07-08T12:49:32.840003Z",
     "shell.execute_reply.started": "2024-07-08T12:49:32.835579Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你是一个信息抽取大师，你现在的任务是从OCR文字识别的结果中提取我指定的关键信息。OCR的文字识别结果使用```符号包围，包含所识别出来的文字，\r\n",
      "                顺序在原始图片中从左至右、从上至下。我指定的关键信息使用[]符号包围。请注意OCR的文字识别结果可能存在长句子换行被切断、不合理的分词、\r\n",
      "                对应错位等问题，你需要结合上下文语义进行综合判断，以抽取准确的关键信息。输出为json格式。\r\n",
      "                下面正式开始：\r\n",
      "                OCR文字：``` 附件： 华南理工大学家庭经济困难本科生认定申请表 院系：物理专业： 物2里年级：2022班级： 学号：202204347298 姓名霍斤 性别勇 出生年月 2004108 民族 汉 茶 城镇口农村 性质 户籍地址 东省(自治区)小市海(市、区) 镇（街道） 村（居委） 家长手机号码 14290482098 家庭人均年收入 （元） 家庭人口数 Z 家庭成员在学人数 家 庭 赠养人数 家庭成员失业人数 情 1.脱贫家庭学生2.脱贫不稳定家庭学生3.边缘易致贫家庭学生4.最低生活保障家庭学生5.最低 况 生活保障边缘家庭学生6.支出型困难家庭学生7.特困供养学生8.孤儿（含事实无人抚养）9.享 受国家定期抚恤补贴的优抚对象（含烈士子女、牺牲军人子女）10.因公牺牲警察子女11.特困职 工子女12.家庭经济困难残疾学生13.残疾人子女 如符合上述类型，请填写相应数字： 与学生 工作（学习） 年收入 姓名 年龄 从业情况 文化程度 健康状况 主要家庭成员情况 关系 单位 （元） 霍久 仅子 服务员 中学 57 5000 无 无 家庭遭受自然灾害情况： 家庭遭受突发意外事件： 影响 霍斤得了期怕金标症 家庭 家庭成员因残疾、年迈而劳动能力弱情况： 经济 元 5万元 家庭成员失业情况： ，家庭欠债情况： 状况 无 有关 其他情况： 信息 承诺内容： 求所信属实 个人承诺 街道办或村委会联系 17320982077 电话（必填） 学生本人签名：霍斤 2024年04月21日```\r\n",
      "                要抽取的关键信息：[姓名 时间 乘车的车次 到站]。\r\n"
     ]
    }
   ],
   "source": [
    "key = \"姓名 时间 乘车的车次 到站\"\n",
    "ocrprompt = f\"\"\"你是一个信息抽取大师，你现在的任务是从OCR文字识别的结果中提取我指定的关键信息。OCR的文字识别结果使用```符号包围，包含所识别出来的文字，\n",
    "                顺序在原始图片中从左至右、从上至下。我指定的关键信息使用[]符号包围。请注意OCR的文字识别结果可能存在长句子换行被切断、不合理的分词、\n",
    "                对应错位等问题，你需要结合上下文语义进行综合判断，以抽取准确的关键信息。输出为json格式。\n",
    "                下面正式开始：\n",
    "                OCR文字：```{ocr_result}```\n",
    "                要抽取的关键信息：[{key}]。\"\"\"\n",
    "print(ocrprompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eae35443-014a-4fb3-a60b-71ba3b4515e0",
   "metadata": {},
   "source": [
    "## 4、定义chatocr函数\n",
    "定义chatocr函数，实现将提示词传入到大模型进行推理并输出信息的功能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ab0d3657-a16a-4a9d-a0d4-faa92876e461",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:49:32.842242Z",
     "iopub.status.busy": "2024-07-08T12:49:32.841903Z",
     "iopub.status.idle": "2024-07-08T12:49:32.846264Z",
     "shell.execute_reply": "2024-07-08T12:49:32.845513Z",
     "shell.execute_reply.started": "2024-07-08T12:49:32.842221Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def chatocr(prompt=\"hello\"):\n",
    "    output = predictor.predict([prompt])[\"result\"][0]  # 推理\n",
    "    print(f\"答==>： \\n{output}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f1af62a-253c-4ab2-838f-09c106eaf6f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:49:32.847666Z",
     "iopub.status.busy": "2024-07-08T12:49:32.847300Z",
     "iopub.status.idle": "2024-07-08T12:49:40.839351Z",
     "shell.execute_reply": "2024-07-08T12:49:40.838409Z",
     "shell.execute_reply.started": "2024-07-08T12:49:32.847645Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "问：你好\r\n",
      "答：\r\n",
      "问：hello，how are you？\r\n",
      "答：\r\n",
      "\r\n",
      "问：怎么精通大模型？\r\n",
      "答：\r\n",
      "答==>： \r\n",
      "这是一个非常直接的问题，而且没有明确的答案。因为喜不喜欢是一种主观感受，不同的人可能会有不同的看法。所以，我建议不要把这个问题看得太重，而是尝试在生活中更多地关注自己，与朋友和家人建立良好的关系，并努力实现自己的目标。\r\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('我喜欢你，你喜欢我吗？',\n",
       "  '这是一个非常直接的问题，而且没有明确的答案。因为喜不喜欢是一种主观感受，不同的人可能会有不同的看法。所以，我建议不要把这个问题看得太重，而是尝试在生活中更多地关注自己，与朋友和家人建立良好的关系，并努力实现自己的目标。')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def make_prompt(query: str, history = None):\n",
    "    if history is None:\n",
    "        return query\n",
    "    else:\n",
    "        prompt = \"\"\n",
    "        for i, (old_query, response) in enumerate(history):\n",
    "            prompt += f\"问：{old_query}\\n答：\\n\"\n",
    "        prompt += f\"\\n问：{query}\\n答：\"\n",
    "    return prompt\n",
    "prompts = make_prompt(\n",
    "    query=\"怎么精通大模型？\", history=[(\"你好\", \"你好👋!我是人工智能助手\"), (\"hello，how are you？\", \"fine think you\")]\n",
    ")\n",
    "print(prompts)\n",
    "\n",
    "\n",
    "def chatglm(history, question):\n",
    "    history = [] if history == \"\" else eval(history)  # 传入的是字符串，因此要转处理成列表\n",
    "    if history == []:\n",
    "        prompt = question        \n",
    "    else :\n",
    "        prompt = make_prompt(query = question, history = history)\n",
    "    # print(f\"prompt:\\n{prompt}\")\n",
    "    output = predictor.predict([prompt])[\"result\"][0]  # 推理\n",
    "    print(f\"答==>： \\n{output}\")\n",
    "    history.append((question[:500], output[:500]))  # 只记录每轮问题和答案的500个字符\n",
    "    history = history[-10:] # 只保留10轮对话历史\n",
    "    globals()['history'] = history  # 将history传入全局变量\n",
    "\n",
    "chatglm('','我喜欢你，你喜欢我吗？')\n",
    "\n",
    "history"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "173dd09a-2d6b-4e22-a99e-130470519e0d",
   "metadata": {},
   "source": [
    "## 5、低配版“PP-ChatOCR”--chatocr的表现\n",
    "\n",
    "### 任务1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5b9af73d-5949-406b-9f14-37336fa775e5",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-07-08T12:51:59.463413Z",
     "shell.execute_reply.started": "2024-07-08T12:49:40.841057Z"
    },
    "scrolled": true,
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "答==>： \r\n",
      "喵。 喵。喵。 喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。喵。\r\n"
     ]
    }
   ],
   "source": [
    "chatocr('你有记忆吗嘿嘿嘿？')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "61d862c0-325d-460c-a2d6-d7af93948b5d",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-07-08T13:06:45.131700Z",
     "shell.execute_reply.started": "2024-07-08T13:06:08.577482Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "答==>： \r\n",
      "```\r\n",
      "{\r\n",
      "\"院系\": \"物理专业\",\r\n",
      "\"专业\": \"华南理工大学家庭经济困难本科生认定申请表\",\r\n",
      "\"年级\": \"2022\",\r\n",
      "\"班级\": \"物2里年级：2022班级\",\r\n",
      "\"学号\": \"202204347298\",\r\n",
      "\"姓名\": \"霍斤\",\r\n",
      "\"性别\": \"勇\",\r\n",
      "\"出生年月\": \"2004108\",\r\n",
      "\"民族\": \"汉\",\r\n",
      "\"主要家庭成员情况\": {\r\n",
      "\"家长手机号码\": \"14290482098\",\r\n",
      "\"家庭人均年收入\": \"5000\",\r\n",
      "\"家庭人口数\": \"5\",\r\n",
      "\"家庭成员失业人数\": \"0\",\r\n",
      "\"家庭遭受自然灾害情况\": \"无\",\r\n",
      "\"家庭遭受突发意外事件\": \"无\",\r\n",
      "\"影响\": \"霍斤得了期怕金标症\",\r\n",
      "\"经济\": \"5万元\",\r\n",
      "\"家庭成员失业情况\": \"0\",\r\n",
      "\"家庭欠债情况\": \"无\",\r\n",
      "\"其他情况\": \"无\"\r\n",
      "},\r\n",
      "\"日期\": \"2024年04月21日\"\r\n",
      "}\r\n",
      "```\r\n"
     ]
    }
   ],
   "source": [
    "prompt = f\"\"\"\r\n",
    "你现在的任务是从OCR文字识别的结果中提取我指定的关键信息。OCR的文字识别结果使用```符号包围，包含所识别出来的文字，\r\n",
    "顺序在原始图片中从左至右、从上至下。我指定的关键信息使用[]符号包围。请注意OCR的文字识别结果可能存在长句子换行被切断、不合理的分词、\r\n",
    "对应错位等问题，你需要结合上下文语义进行综合判断，以抽取准确的关键信息。在返回结果时使用json格式，包含一个key-value对，key值为我指定的关键信息，value值为所抽取的结果。\r\n",
    "如果认为OCR识别结果中没有关键信息key，则将value赋值为“未找到相关信息”。 请只输出json格式的结果，不要包含其它多余文字！\r\n",
    "下面正式开始：\r\n",
    "OCR文字：```{ocr_result}```\r\n",
    "要抽取的关键信息：[院系, 专业, 年级, 班级, 学号, 姓名, 性别, 出生年月, 民族, 主要家庭成员情况,日期]。\r\n",
    "\"\"\"\r\n",
    "chatocr(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a6eb4480-0c6d-4983-973e-cab524d45acb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:54:13.971976Z",
     "iopub.status.busy": "2024-07-08T12:54:13.971302Z",
     "iopub.status.idle": "2024-07-08T12:54:13.976923Z",
     "shell.execute_reply": "2024-07-08T12:54:13.976196Z",
     "shell.execute_reply.started": "2024-07-08T12:54:13.971938Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "你现在的任务是从OCR文字识别的结果中提取我指定的关键信息。OCR的文字识别结果使用```符号包围，包含所识别出来的文字，\r\n",
      "顺序在原始图片中从左至右、从上至下。我指定的关键信息使用[]符号包围。请注意OCR的文字识别结果可能存在长句子换行被切断、不合理的分词、\r\n",
      "对应错位等问题，你需要结合上下文语义进行综合判断，以抽取准确的关键信息。在返回结果时使用json格式，包含一个key-value对，key值为我指定的关键信息，value值为所抽取的结果。\r\n",
      "如果认为OCR识别结果中没有关键信息key，则将value赋值为“未找到相关信息”。 请只输出json格式的结果，不要包含其它多余文字！\r\n",
      "下面正式开始：\r\n",
      "OCR文字：``` 附件： 华南理工大学家庭经济困难本科生认定申请表 院系：物理专业： 物2里年级：2022班级： 学号：202204347298 姓名霍斤 性别勇 出生年月 2004108 民族 汉 茶 城镇口农村 性质 户籍地址 东省(自治区)小市海(市、区) 镇（街道） 村（居委） 家长手机号码 14290482098 家庭人均年收入 （元） 家庭人口数 Z 家庭成员在学人数 家 庭 赠养人数 家庭成员失业人数 情 1.脱贫家庭学生2.脱贫不稳定家庭学生3.边缘易致贫家庭学生4.最低生活保障家庭学生5.最低 况 生活保障边缘家庭学生6.支出型困难家庭学生7.特困供养学生8.孤儿（含事实无人抚养）9.享 受国家定期抚恤补贴的优抚对象（含烈士子女、牺牲军人子女）10.因公牺牲警察子女11.特困职 工子女12.家庭经济困难残疾学生13.残疾人子女 如符合上述类型，请填写相应数字： 与学生 工作（学习） 年收入 姓名 年龄 从业情况 文化程度 健康状况 主要家庭成员情况 关系 单位 （元） 霍久 仅子 服务员 中学 57 5000 无 无 家庭遭受自然灾害情况： 家庭遭受突发意外事件： 影响 霍斤得了期怕金标症 家庭 家庭成员因残疾、年迈而劳动能力弱情况： 经济 元 5万元 家庭成员失业情况： ，家庭欠债情况： 状况 无 有关 其他情况： 信息 承诺内容： 求所信属实 个人承诺 街道办或村委会联系 17320982077 电话（必填） 学生本人签名：霍斤 2024年04月21日```\r\n",
      "要抽取的关键信息：[院系, 专业, 年级, 班级, 学号, 姓名, 性别, 出生年月, 民族, 户籍性质, 户籍地址, 家长手机号码, 家庭人均年收入, 家庭人口数, 家庭成员在学人数, 家庭赡养人数, 家庭成员失业人数, 家庭经济困难类型, 主要家庭成员情况, 家庭遭受自然灾害情况, 家庭遭受突发意外事件, 家庭成员因残疾年迈而劳动能力弱情况, 家庭欠债情况, 承诺内容, 街道办或村委会联系, 学生本人签名, 日期]。\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "print(prompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33fc9557-730b-4145-8587-4a13cc8bc360",
   "metadata": {},
   "source": [
    "### 任务2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a7c1cdd5-40a8-4875-a177-14013990b5ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:52:33.655702Z",
     "iopub.status.busy": "2024-07-08T12:52:33.655322Z",
     "iopub.status.idle": "2024-07-08T12:52:44.474011Z",
     "shell.execute_reply": "2024-07-08T12:52:44.472434Z",
     "shell.execute_reply.started": "2024-07-08T12:52:33.655674Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "答==>： \r\n",
      "根据OCR文字识别结果，可以提取出以下关键信息：\r\n",
      "\r\n",
      "[姓名 时间 乘车的车次 到站]\r\n",
      "\r\n",
      "其中，姓名是\"霍斤\"，时间是\"2022年4月21日\"，乘车的车次是\"华南理工大学家庭经济困难本科生认定申请表\"，到站是\"小市\"。\r\n"
     ]
    }
   ],
   "source": [
    "chatocr(ocrprompt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06c21007-d540-49e3-891f-81507e021e5b",
   "metadata": {},
   "source": [
    "发现车次信息不对， `\"乘车的车次\": \"7788.com Z57A001950\"`，应该是`\"乘车的车次\": \"G7512次\"`\n",
    "\n",
    "另外到站信息也不对，杭州东是发站，到站是上海虹桥。 当然这里的到站有一定的歧义，不能全怪ChatGLM2-6B模型。\n",
    "\n",
    "## 6、PP-ChatOCR原型--使用文心大模型API效果展示\n",
    "现在AIStudio平台可以使用文心大模型API了，我们看看文心加持下的表现.\n",
    "首先安装aistudio库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c6b71442-decb-4a23-b9ff-90bc640eec21",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:52:44.476636Z",
     "iopub.status.busy": "2024-07-08T12:52:44.475993Z",
     "iopub.status.idle": "2024-07-08T12:52:47.633274Z",
     "shell.execute_reply": "2024-07-08T12:52:47.632125Z",
     "shell.execute_reply.started": "2024-07-08T12:52:44.476593Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirror.baidu.com/pypi/simple/, https://mirrors.aliyun.com/pypi/simple/\r\n",
      "Collecting aistudio==0.0.2\r\n",
      "  Downloading https://studio-package.bj.bcebos.com/aistudio-0.0.2-py3-none-any.whl (8.7 kB)\r\n",
      "Requirement already satisfied: requests in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from aistudio==0.0.2) (2.31.0)\r\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from requests->aistudio==0.0.2) (3.3.2)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from requests->aistudio==0.0.2) (3.7)\r\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from requests->aistudio==0.0.2) (2.2.1)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from requests->aistudio==0.0.2) (2024.2.2)\r\n",
      "Installing collected packages: aistudio\r\n",
      "Successfully installed aistudio-0.0.2\r\n"
     ]
    }
   ],
   "source": [
    "!pip install https://studio-package.bj.bcebos.com/aistudio-0.0.2-py3-none-any.whl"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b37bb1f-26f0-4686-aaae-c75d06f4c9c9",
   "metadata": {},
   "source": [
    "### 任务1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "07aa8bfe-284c-4e79-81d4-9c74ee49b142",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:52:47.635313Z",
     "iopub.status.busy": "2024-07-08T12:52:47.634834Z",
     "iopub.status.idle": "2024-07-08T12:52:47.787402Z",
     "shell.execute_reply": "2024-07-08T12:52:47.786257Z",
     "shell.execute_reply.started": "2024-07-08T12:52:47.635279Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\r\n"
     ]
    }
   ],
   "source": [
    "import aistudio\n",
    "# 创建单轮对话\n",
    "chat_completion = aistudio.chat.create(\n",
    " messages=[\n",
    " {\n",
    " \"role\": \"user\",\n",
    " \"content\": prompt\n",
    " }\n",
    " ] )\n",
    "print(chat_completion.result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aebb0f8-0789-4312-9e93-9d537767957a",
   "metadata": {},
   "source": [
    "### 任务2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "92deaa62-d4e2-4491-8968-7f5cb6a47506",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-08T12:52:47.789157Z",
     "iopub.status.busy": "2024-07-08T12:52:47.788721Z",
     "iopub.status.idle": "2024-07-08T12:52:47.938519Z",
     "shell.execute_reply": "2024-07-08T12:52:47.937441Z",
     "shell.execute_reply.started": "2024-07-08T12:52:47.789130Z"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\r\n"
     ]
    }
   ],
   "source": [
    "import aistudio\n",
    "# 创建单轮对话\n",
    "chat_completion = aistudio.chat.create(\n",
    " messages=[\n",
    " {\n",
    " \"role\": \"user\",\n",
    " \"content\": ocrprompt\n",
    " }\n",
    " ] )\n",
    "print(chat_completion.result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d0b7a89-f917-4aa3-8614-c738cbfec391",
   "metadata": {},
   "source": [
    "可以看到文心大模型的车次抽取是正确的。\n",
    "\n",
    "\n",
    "# 三、chatocr与PP-ChatOCR对比总结\n",
    "首先，我们的低配版chatocr能够运行！\n",
    "\n",
    "其次，相对于PP-ChatOCR，我们的低配版chatocr的效果要稍微差一些，例如车次抽取会出现错误，到站也没有抽取对。但这并不意外，毕竟这是一个只有7B大小的模型，（在花费更多精力调整提示词后）大部分的抽取结果都是正确的，可以基本满足需求，已经不错了！\n",
    "\n",
    "总的来说，ChatGLM2-6B模型已经非常优秀了，同样的提示词它能实现和文心大模型类似的输出。从学习的角度来看，我们的开源低配版chatocr算取得不小的成功。\n",
    "\n"
   ]
  },
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   "source": [
    "# 结束语\n",
    "让我们荡起双桨，在AI的海洋乘风破浪！\n",
    "\n",
    "飞桨官网：https://www.paddlepaddle.org.cn\n",
    "\n",
    "因为水平有限，难免有不足之处，还请大家多多帮助。\n",
    "\n",
    "作者：段春华， 网名skywalk 或 天马行空，济宁市极快软件科技有限公司的AI架构师，百度飞桨PPDE。\n",
    "\n",
    "我在AI Studio上获得至尊等级，点亮10个徽章，来互关呀~ https://aistudio.baidu.com/aistudio/personalcenter/thirdview/141218"
   ]
  }
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